A footstep pressure sequence contains both spatial and temporal patterns, which can be viewed as a 2-dimensional grey image sequence or a 3-dimensional vision cube.
Deep learning architectures devised for the spatio-temporal data have demonstrated great success across a wide range of tasks including action recognition and object detection and tracking. Initially to process 2-dimensional spatial sequences, single-stream [1]} and two-stream methods [2]} are proposed.
The single stream based methods utilize pre-trained 2-dimensional convolution filters frame by frame and a number of temporal fusion strategies are being further investigated. The two-stream based methods take advantage of the appearance and the optical flow features obtained by 2-dimensional convolutions to form spatio-temporal representations.
